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Machine Learning Key Lessons Learned for Developers

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Machine Learning Key Lessons Learned for Developers

  1. 1. © 2019, Amazon Web Services, Inc. or its Affiliates. Young Yang, ML Specialist SA beyoung@amazon.com Machine Learning Key Lessons Learned for Developers
  2. 2. © 2019, Amazon Web Services, Inc. or its Affiliates. What’s Real Customer Value of Business Problem?
  3. 3. © 2019, Amazon Web Services, Inc. or its Affiliates. Data Visualization & Analysis Business Problem – ML problem framing Data Collection Data Integration Data Preparation & Cleaning Feature Engineering Model Training & Parameter Tuning Model Evaluation Are Business Goals met? Model Deployment Monitoring & Debugging – Predictions YesNo DataAugmentation Feature Augmentation Retraining
  4. 4. © 2019, Amazon Web Services, Inc. or its Affiliates. 老闆的期待
  5. 5. © 2019, Amazon Web Services, Inc. or its Affiliates. 請問大家對於客服/聊天 機器人的使用經驗?
  6. 6. © 2019, Amazon Web Services, Inc. or its Affiliates. 老闆對於 AI / ML / Big Data 的想像
  7. 7. © 2019, Amazon Web Services, Inc. or its Affiliates. 如何選擇ft? 目標:求使得Obj最小的ft 但實務上..
  8. 8. © 2019, Amazon Web Services, Inc. or its Affiliates. Output Know or Estimated values Models Input parameters • Yes/No • Multi-classifications • Clustering • Value • Vectors What’s your input? What’s your output to help the measure of correctness and efficiency How to frame business problems into machine learning?
  9. 9. © 2019, Amazon Web Services, Inc. or its Affiliates. 80% of Your Time Is On Data
  10. 10. © 2019, Amazon Web Services, Inc. or its Affiliates. Data Visualization & Analysis Business Problem – ML problem framing Data Collection Data Integration Data Preparation & Cleaning Feature Engineering Model Training & Parameter Tuning Model Evaluation Are Business Goals met? Model Deployment Monitoring & Debugging – Predictions YesNo DataAugmentation Feature Augmentation Retraining
  11. 11. © 2019, Amazon Web Services, Inc. or its Affiliates. 以影像辨識判斷貓狗,需要千到萬張的圖片做訓練。 入門 初級 進階 專業 100 ~1,000 張影像 辨識有沒有 1,000 ~10,000 張影像 貓或狗? 10,000 ~ 1M (retriever vs labrador) 拉布拉多 黃金獵犬? This is My Dog 1M+ 張影像 (or IoT) 如需進階的檢驗,例如拉布拉多 vs 黃金獵犬,則需更大量的資料訓練 https://www.facebook.com/huskyworldwide0/photos/a.109404807150206/111965016894185/?type=3&permPage=1
  12. 12. © 2019, Amazon Web Services, Inc. or its Affiliates. You need a lot of featuring works. Also, try and error wi-fiWi-Fi lower case wifi Remove Punctuations Spelling Checks
  13. 13. © 2019, Amazon Web Services, Inc. or its Affiliates. No General Purpose Intelligence
  14. 14. © 2019, Amazon Web Services, Inc. or its Affiliates. Data Visualization & Analysis Business Problem – ML problem framing Data Collection Data Integration Data Preparation & Cleaning Feature Engineering Model Training & Parameter Tuning Model Evaluation Are Business Goals met? Model Deployment Monitoring & Debugging – Predictions YesNo DataAugmentation Feature Augmentation Retraining
  15. 15. © 2019, Amazon Web Services, Inc. or its Affiliates. Agent backbone (bus) Text to Speech Engine Speech to Text Engine Tasks NLU Engine Text NLP Engine Image & Face Analysis Engine Semantic Net/ Knowledge Graph Engine Rules- based Inferencing Engine Learning & Personal- ization Engine Calendar Engine Modality Manager Dialog Manager Context Manager Service Manager “Delight” Manager Learning & Personalization Engine
  16. 16. © 2019, Amazon Web Services, Inc. or its Affiliates. 12 dogs ML detect 3 dogs But Actual is CAT 5 dogs Recall 5/12. Precision 5/8.
  17. 17. © 2019, Amazon Web Services, Inc. or its Affiliates. Machine Learning Model Defect Decision Block Order Confirmed Defect Rebuild Wafer 1K loss Pass Order Receive Payment Customer Suite 10M loss Correct (True Positive) Correct (True Negative) Incorrect (False Positive) Incorrect (False Negative) Yes (Positive) No (Negative)
  18. 18. All fraud cases identified => Recall 1 Zero fraud cases identified => Recall 0 Zero false positives => Precision 1 Only false positives => Precision 0 Performance measure
  19. 19. © 2019, Amazon Web Services, Inc. or its Affiliates. Machine Learning Model Fraud Decision Block Order Confirmed Fraud (2nd DD, feedback loop) Reinstate & Ship Order (delay order) Pass Order Receive Payment Charge Back/ Bad Debt (10M Loss) Correct (True Positive) Correct (True Negative) Incorrect (False Positive) Incorrect (False Negative) Yes (Positive) No (Negative)
  20. 20. © 2019, Amazon Web Services, Inc. or its Affiliates. Feedback Loop and Automation
  21. 21. © 2019, Amazon Web Services, Inc. or its Affiliates. Data Visualization & Analysis Business Problem – ML problem framing Data Collection Data Integration Data Preparation & Cleaning Feature Engineering Model Training & Parameter Tuning Model Evaluation Are Business Goals met? Model Deployment Monitoring & Debugging – Predictions YesNo DataAugmentation Feature Augmentation Retraining
  22. 22. © 2019, Amazon Web Services, Inc. or its Affiliates. Feed Back Loop is Important
  23. 23. © 2019, Amazon Web Services, Inc. or its Affiliates. Automation: Deploy as fast as you can Deploy to a device Train with SageMaker algorithms Train with your own algorithms Train with TensorFlow or MXNet or PyTorch A/B TestOptimize your models Deploy to the Cloud BYO Rinse and repeat for every device, every model change
  24. 24. © 2019, Amazon Web Services, Inc. or its Affiliates. The More Automation, The More Innovation Infrastructure Support Innovation Infrastructure Support Innovation Innovation Support ✅ automate automate
  25. 25. © 2019, Amazon Web Services, Inc. or its Affiliates. Take Away
  26. 26. © 2019, Amazon Web Services, Inc. or its Affiliates. Data Visualization & Analysis Business Problem – ML problem framing Data Collection Data Integration Data Preparation & Cleaning Feature Engineering Model Training & Parameter Tuning Model Evaluation Are Business Goals met? Model Deployment Monitoring & Debugging – Predictions YesNo DataAugmentation Feature Augmentation Retraining © 2019, Amazon Web Services, Inc. or its Affiliates. Output Know or Estimated values Models Input parameters • Yes/No • Multi-classifications • Clustering • Value • Vectors What’s your input? What’s your output to help the measure of correctness and efficiency How to frame business problems into machine learning? © 2019, Amazon Web Services, Inc. or its Affiliates. 100 ~1,000 1,000 ~10,000 ? (retriever vs labrador) ? This is My Dog (or IoT) vs https://www.facebook.com/huskyworldwide0/photos/a.109404807150206/111965016894185/?type=3&permPage=1 © 2019, Amazon Web Services, Inc. or its Affiliates. Agent backbone (bus) Text to Speech Engine Speech to Text Engine Tasks NLU Engine Text NLP Engine Image & Face Analysis Engine Semantic Net/ Knowledge Graph Engine Rules- based Inferencing Engine Learning & Personal- ization Engine Calendar Engine Modality Manager Dialog Manager Context Manager Service Manager “Delight” Manager Learning & Personalization Engine
  27. 27. © 2019, Amazon Web Services, Inc. or its Affiliates. © 2019, Amazon Web Services, Inc. or its Affiliates. Machine Learning Model Defect Decision Block Order Confirmed Defect Rebuild Wafer 1K loss Pass Order Receive Payment Customer Suite 10M loss © 2019, Amazon Web Services, Inc. or its Affiliates. Feed Back Loop is Important © 2019, Amazon Web Services, Inc. or its Affiliates. The More Automation, The More Innovation Infrastructure Support Innovation Infrastructure Support Innovation Innovation Support automate automate
  28. 28. © 2019, Amazon Web Services, Inc. or its Affiliates. customer value
  29. 29. © 2019, Amazon Web Services, Inc. or its Affiliates. Data SelectionData Selection Video Data Prep Data Cleansing (Missing Values, Normalization, etc..) Databases (Join, Filter, etc..) Dim Reduce (ID Salient Features) Annotation (Label the ground truth) Model Selection Regression Classification Clustering Model Training Connectionists (Neural Networks) Symbolists (Logic) Analogists (SVMs) Bayesians (Graphical Networks) Evolutionists (Genetic Agents) Boosters (Boosted Trees) Model Evaluation HPO (Non-learned parameter tuning) Cross Validation (Generalization?) Metrics (Bias, Variance, F1 Score) Regularization (Dropout, L2, etc..) Model Deployment Portability Ensemble New Predictions ML is still too complicated for everyday developers
  30. 30. © 2019, Amazon Web Services, Inc. or its Affiliates. HOW WE CAN HELP • Brainstorming • Custom modeling • Training • Work side-by-side with Amazon experts ML Solutions Lab • Practical education on ML for new and experienced practitioners • Based on the same material used to train Amazon developers Machine Learning Training and Certification
  31. 31. © 2019, Amazon Web Services, Inc. or its Affiliates. 數據 + 運算 + 持續修正 =人工智慧 Data + Model + Continuously Feedback Loop = AI
  32. 32. © 2019, Amazon Web Services, Inc. or its Affiliates. Q & A Young Yang, ML Specialist SA beyoung@amazon.com

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